The conditional colour–magnitude distribution – I. A comprehensive model of the colour–magnitude–halo mass distribution of present-day galaxies

Xu, Haojie; Zheng, Zheng; Guo, Hong; Zu, Ying; Zehavi, Idit; Weinberg, David H.

doi:10.1093/mnras/sty2615

Cited by 41 publications

(47 citation statements)

References 107 publications

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“…Constraining galaxy assembly bias is important to the study of the connection between galaxies and haloes and for extracting the maximum possible information on both galaxy evolution and cosmology from survey data. Numerous studies use galaxy clustering to constrain either the galaxy-halo connection or cosmology or both (e.g., Hawkins et al 2003;van den Bosch et al 2003;Yang et al 2004;Seljak et al 2005Seljak et al , 2006Cooray 2006;van den Bosch et al 2007;Zheng et al 2007;Moster et al 2010;Zehavi et al 2011;Anderson et al 2012;Guo et al 2012a,b;Leauthaud et al 2012;Cacciato et al 2013;Reddick et al 2013;Guo et al 2014;Reid et al 2014;Coupon et al 2015;More et al 2015;Saito et al 2016;Lange et al 2019;Cowley et al 2018;Sinha et al 2018;Xu et al 2018). Several of these works combine clustering with either weak galaxy-galaxy lensing measurements or with measurements of redshift space distortions in order to constrain the galaxy-halo connection and/or cosmology, and this use of complementary variables is becoming increasingly common.…”

Section: Discussionmentioning

confidence: 99%

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

Wang

Mao

Zentner

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Most models for the statistical connection between galaxies and their haloes ignore the possibility that galaxy properties may be correlated with halo properties other than halo mass, a phenomenon known as galaxy assembly bias. And yet, it is known that such correlations can lead to systematic errors in the interpretation of survey data that are analyzed using traditional halo occupation models. At present, the degree to which galaxy assembly bias may be present in the real Universe, and the best strategies for constraining it remain uncertain. We study the ability of several observables to constrain galaxy assembly bias from redshift survey data using the decorated halo occupation distribution (dHOD), an empirical model of the galaxyhalo connection that incorporates assembly bias. We cover an expansive set of observables, including the projected two-point correlation function w p (r p ), the galaxy-galaxy lensing signal ∆Σ(r p ), the void probability function VPF(r), the distributions of counts-in-cylinders P(N CIC ), and counts-in-annuli P(N CIA ), and the distribution of the ratio of counts in cylinders of different sizes P(N 2 /N 5 ). We find that despite the frequent use of the combination w p (r p ) + ∆Σ(r p ) in interpreting galaxy data, the count statistics, P(N CIC ) and P(N CIA ), are generally more efficient in constraining galaxy assembly bias when combined with w p (r p ). Constraints based upon w p (r p ) and ∆Σ(r p ) share common degeneracy directions in the parameter space, while combinations of w p (r p ) with the count statistics are more complementary. Therefore, we strongly suggest that count statistics should be used to complement the canonical observables in future studies of the galaxy-halo connection.

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Section: Discussionmentioning

confidence: 99%

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

Wang

Mao

Zentner

et al. 2019

Monthly Notices of the Royal Astronomical Society

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“…There have been several attempts in the past to extend the halo model in order to predict the photometric properties of galaxies (e.g. Scranton 2002;Cooray 2006;Skibba & Sheth 2009;Xu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Multitracer extension of the halo model: probing quenching and conformity in eBOSS

Alam

Peacock

Kraljic

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Abstract We develop a new Multi-Tracer Halo Occupation Distribution (MTHOD) framework for the galaxy distribution and apply it to the extended Baryon Oscillation Spectroscopic Survey (eBOSS) final data between z = 0.7 − 1.1. We obtain a best fit MTHOD for each tracer and describe the host halo properties of these galaxies. The mean halo masses for LRGs, ELGs and QSOs are found to be 1.9 × 1013 h−1M⊙, 1.1 × 1012 h−1M⊙ and 5 × 1012 h−1M⊙ respectively in the eBOSS data. We use the MTHOD framework to create mock galaxy catalogues and predict auto- and cross-correlation functions for all the tracers. Comparing these results with data, we investigate galactic conformity, the phenomenon whereby the properties of neighbouring galaxies are mutually correlated in a manner that is not captured by the basic halo model. We detect 1-halo conformity at more than 3σ statistical significance, while obtaining upper limits on 2-halo conformity. We also look at the environmental dependence of the galaxy quenching efficiency and find that halo mass driven quenching successfully explains the behaviour in high density regions, but it fails to describe the quenching efficiency in low density regions. In particular, we show that the quenching efficiency in low density filaments is higher in the observed data, as compared to the prediction of the MTHOD with halo mass driven quenching. The mock galaxy catalogue constructed in this paper is publicly available on this website†.

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“…However, halo clustering has been shown to depend on secondary halo properties or more generally on the assembly history or large-scale environment of the haloes, a phenomenon termed (halo) assembly bias (Sheth & Tormen 2004;Gao et al 2005;Paranjape et al 2018;Ramakrishnan et al 2019). The dependences on these secondary parameters manifest themselves in different ways and are not trivially described (Mao et al 2018;Salcedo et al 2018;Xu & Zheng 2018;Han et al 2019). Halo assembly bias might impact large scale galaxy clustering as well, if the formation of galaxy is correlated to that of the host halo, an effect commonly referred to as galaxy assembly bias (GAB hereafter; e.g., Croton et al 2007;Zu et al 2008;Chaves-Montero et al 2016;Contreras et al 2019;Xu & Zheng 2020;Xu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Predicting halo occupation and galaxy assembly bias with machine learning

Xu,

Kumar,

Zehavi

et al. 2021

Preprint

Self Cite

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Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the halo occupations and recover galaxy clustering and assembly bias in a semi-analytic galaxy formation model. For stellar-mass selected samples, we train a Random Forest algorithm on the number of central and satellite galaxies in each dark matter halo. With the predicted occupations, we create mock galaxy catalogues and measure the clustering and assembly bias. Using a range of halo and environment properties, we find that the machine learning predictions of the occupancy variations with secondary properties, galaxy clustering and assembly bias are all in excellent agreement with those of our target galaxy formation model. Internal halo properties are most important for the central galaxies prediction, while environment plays a critical role for the satellites. Our machine learning models are all provided in a usable format. We demonstrate that machine learning is a powerful tool for modelling the galaxy-halo connection, and can be used to create realistic mock galaxy catalogues which accurately recover the expected occupancy variations, galaxy clustering and galaxy assembly bias, imperative for cosmological analyses of upcoming surveys.

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The conditional colour–magnitude distribution – I. A comprehensive model of the colour–magnitude–halo mass distribution of present-day galaxies

Cited by 41 publications

References 107 publications

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

Multitracer extension of the halo model: probing quenching and conformity in eBOSS

Predicting halo occupation and galaxy assembly bias with machine learning

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